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Ahmad Kamsyakawuni
Jurusan Matematika, FMIPA Universitas Jember
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

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Journal : Indonesian Journal of Applied Informatics

 1 
Sistem Biometrik Pengenalan Wajah dengan Metode Grey Level Co-Occurrence Matrix dan Support Vector Machine Adhitiyah Redaya Kusuma Bhakti; Abduh Riski; Ahmad Kamsyakawuni
IJAI (Indonesian Journal of Applied Informatics) Vol 7, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v7i2.69069

Abstract

Abstrak Teknologi biometrik wajah dikembangkan untuk mengenali seseorang secara unik. Pada penelitian ini biometrik diaplikasikan pada aplikasi pengenalan wajah dengan citra wajah manusia sebagai objeknya menggunakan metode Grey Level Co-Occurrence Matrix dan Support Vector Machine. Metode GLCM merupakan metode yang digunakan untuk proses ekstraksi fitur citra. Sedangkan SVM digunakan untuk proses pengenalan/identifikasi. Tujuan dari penelitian ini adalah mendapat hasil akurasi yang baik untuk pengenalan wajah melalui kedua metode yang digunakan. Hasil yang diperoleh dari penelitian ini adalah akurasi pada data pelatihan sebesar 93% dengan total 200 citra wajah. Sedangkan pada data pengujian diperoleh akurasi sebesar 90% untuk 50 citra wajah.===================================================AbstractFacial biometric technology was developed to uniquely recognize a person. In this research, biometrics was applied to face recognition applications with human face images as objects using the Gray Level Co-Occurrence Matrix and Support Vector Machine methods. The GLCM is a method used for the image feature extraction process. While SVM is used for the identification process. The purpose of this research is to get good accuracy results for face recognition through the two methods used. The results obtained from this research are the accuracy of the training data by 93% with a total of 200 face images. While the test data obtained an accuracy of 90% for 50 face images.
Co-Authors Abduh Riski Abduh Riski Abduh Riski, Abduh Abdullah, Rinaldy Ahmad Adhitiyah Redaya Kusuma Bhakti Agustina Pradjaningsih Ahmad Husnan Fanani Ahmad Khoirul Umam Ahmad Saiful Rizal Anggoro, Vani Krismo Ari Susiana Arif, M. Ziaul Asti, Melinda Astuti, Lestari Fidi Azimatul Matsniya Cahya Ramadhani Azhar Dian Anggraeni Eko Adi Sarwoko Fakih, Muhammad Fahmil Farida, Yasmin Febia Zein Aziza Firdaus Ubaidillah, Firdaus Fismaya, Florencia Wahyu Ganda Gusti, Dwi Rahmasari Kinasih Haqqi, Wakhidatun Nafi’u Heri Purwantoro Hermanto Hermanto Hidayah, Annisa Yuniar Ikhsanul Halikin, Ikhsanul Innafajri Insyirah Juliyanto, Bagus Kaesardi, Dinda Septika Khumairoh, Anisa Binti Khurnia Palupi Kiswara Agung Santoso Kosala Dwidja Purnomo Kusno Kusno Lestari, Vidiyanti Lilian Atino Eyta Makin M Ziaul Arif, M Ziaul M. Ziaul Arif Maulana, Mohammad Iqbal Maulana, Muhammad Iqbal Nadzira, Annisa Ayu Nanang Suparyono, Nanang Rachmad Gernowo Rahayu, Rivi Tri Ramadhani, Lutvia Citra Regita, Yulia Dewi Retno Wulandari Rizki Gangsar Septiono Santoso, Ahmad Rico Seggaf, Muhammad Shofia Nabila Azzahra Sholihah, Walidatush Siti Virna Rohmatul Izza Sri Ratnawati Sukmawati, Rika Ayu Tri Hartati, Tri Wulandari, Eka Yuni Zainul Anwar